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Comment by aeyes | Hacker News Reader

My problem is what these scripts do. When working with timeseries you want to be able to filter on dimensions/tags/labels.

TimescaleDB doesn't support this out of the box, the reason being Postgres' limitations on multi column indexes when you have data types like jsonb. What they have to do to work around this is to have a mapping table from dimensions/tags/labels which are stored in a jsonb field to integers which they then use to look the data up in the metrics table.

I would expect that a timeseries database is optimized for this type of lookup without hacks involving two tables.

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(TimescaleDB engineer) Actually, the reason we break out the Jsonb like this is because json blobs are big and take up a lot of space and so we normalize it out. This is the same thing that InfluxDB or Prometheus do under the hood. We also do this under the hood for Timescale-Prometheus.

If you have a data model that is slightly more fixed than Prometheus, you'd create a column per tag and have a foreign-key to the labels table. That's a pretty simple schema which would avoid a whole lot of complexity in here. So if you are developing most apps this isn't a problem and filtering on dimensions/tags/labels isn't a problem. Again, this is only an issue of you have very dynamic user-defined tags, as Prometheus does. In which case you can just use Timescale-Prometheus.

I'll echo the sibling comment by the TDB engineer, having read and contributed to similar systems myself.

Most of these custom databases (Influx) use techniques similar to TDB's under the hood, in order to persist the information in an read/write optimized way; they just don't "expose" it as clearly to the end-user, because they aren't offering a relational database.

A critical difference between the two, is that custom databases don't benefit from the fine-tuned performance characteristics of Postgres. So even though they're doing the same thing, they'll generally be doing it slower.

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